Previous research on customizing deep learning models to the needs of IoT applications [1]}, [2]}, [3]} mainly focused on designing neural network structures that integrate conventional deep learning components, such as convolutional and recurrent layers, to extract spatial and temporal properties of inputs. On the other hand, since the physics of measured phenomena are best expressed in the frequency domain, decades of research on signal processing developed powerful techniques for time-frequency analysis of signals, including motion sensor signals [4]}, [5]}, radio frequency signals [6]}, [7]}, acoustic signals [8]}, [9]}, and visible light signals [10]}. A popular transform that maps time-series measurements to the frequency domain is the
Short-Time Fourier Transform (STFT). We, therefore, propose a new neural network model, namely, Short-Time Fourier Neural Networks (STFNets) that operate directly in the frequency domain.
